{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![image](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://demo.leafmap.org/lab/index.html?path=notebooks/104_point_style.ipynb)\n",
    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/leafmap/blob/master/docs/notebooks/104_point_style.ipynb)\n",
    "[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/opengeos/leafmap/HEAD)\n",
    "\n",
    "**Plotting point data with custom styles**\n",
    "\n",
    "Uncomment the following line to install [leafmap](https://leafmap.org) if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install -U leafmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from leafmap import leafmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load GeoJSON data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = (\n",
    "    \"https://github.com/opengeos/datasets/releases/download/world/world_cities.geojson\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map()\n",
    "point_style = {\n",
    "    \"radius\": 5,\n",
    "    \"color\": \"red\",\n",
    "    \"fillOpacity\": 0.8,\n",
    "    \"fillColor\": \"blue\",\n",
    "    \"weight\": 3,\n",
    "}\n",
    "hover_style = {\"fillColor\": \"yellow\", \"fillOpacity\": 1.0}\n",
    "m.add_geojson(\n",
    "    url, point_style=point_style, hover_style=hover_style, layer_name=\"World Cities\"\n",
    ")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load GoeDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.read_file(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = leafmap.Map()\n",
    "point_style = {\n",
    "    \"radius\": 5,\n",
    "    \"color\": \"red\",\n",
    "    \"fillOpacity\": 0.8,\n",
    "    \"fillColor\": \"blue\",\n",
    "    \"weight\": 3,\n",
    "}\n",
    "hover_style = {\"fillColor\": \"yellow\", \"fillOpacity\": 1.0}\n",
    "m.add_gdf(\n",
    "    gdf, point_style=point_style, hover_style=hover_style, layer_name=\"World Cities\"\n",
    ")\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Random Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas, pandas as pd, numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to generate random coordinates within latitude and longitude bounds\n",
    "def random_coordinates(n, lat_min=-90, lat_max=90, lon_min=-180, lon_max=180):\n",
    "    \"\"\"Generates n random latitude/longitude coordinates.\n",
    "\n",
    "    Args:\n",
    "        n (int): The number of coordinates to generate.\n",
    "        lat_min (float): Minimum latitude. Defaults to -90.\n",
    "        lat_max (float): Maximum latitude. Defaults to 90.\n",
    "        lon_min (float): Minimum longitude. Defaults to -180.\n",
    "        lon_max (float): Maximum longitude. Defaults to 180.\n",
    "\n",
    "    Returns:\n",
    "        pandas.DataFrame: A DataFrame containing 'Longitude' and 'Latitude' columns.\n",
    "    \"\"\"\n",
    "\n",
    "    latitudes = [random.uniform(lat_min, lat_max) for _ in range(n)]\n",
    "    longitudes = [random.uniform(lon_min, lon_max) for _ in range(n)]\n",
    "    return pd.DataFrame({\"Longitude\": longitudes, \"Latitude\": latitudes})\n",
    "\n",
    "\n",
    "numpoints = 1000\n",
    "\n",
    "# Generate random coordinates across the globe\n",
    "df = random_coordinates(numpoints)\n",
    "\n",
    "# Add a 'Conc' column (optional, for demonstration)\n",
    "df[\"Conc\"] = np.random.randn(numpoints) + 17  # Example data\n",
    "\n",
    "# Create GeoDataFrame\n",
    "gdf = geopandas.GeoDataFrame(\n",
    "    df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude), crs=\"EPSG:4326\"\n",
    ")\n",
    "\n",
    "m = leafmap.Map()  # Start with a low zoom to show the global distribution\n",
    "\n",
    "# Add the GeoDataFrame to the map\n",
    "m.add_gdf(\n",
    "    gdf,\n",
    "    hover_style={\"fillColor\": \"yellow\", \"fillOpacity\": 1.0},\n",
    "    point_style={\n",
    "        \"radius\": 5,\n",
    "        \"color\": \"red\",\n",
    "        \"fillColor\": \"red\",\n",
    "        \"fillOpacity\": 0.5,\n",
    "        \"opacity\": 0.5,\n",
    "    },\n",
    ")\n",
    "\n",
    "m"
   ]
  }
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